06-newtargets.r

install.packages("yaImpute")
library(yaImpute)
library(randomForest)

library(dplyr)
library(magrittr)

data(iris)
# set the random number seed so that example results are consistant
# normally, leave out this command
set.seed(12345)
# form some test data
refs=sample(rownames(iris),50) # just the reference observations
x <- iris[refs,1:3] # Sepal.Length Sepal.Width Petal.Length
y <- iris[refs,4:5] # Petal.Width Species


# build a yai object using mahalanobis
mal <- yai(x=x,y=y,method="mahalanobis")


which(rownames(iris) %in% rownames(x))

iris[!(rownames(iris) %in% rownames(x)),] %>% dim

# get imputations for the target observations (not references)
malNew <- newtargets(mal,iris[!(rownames(iris) %in% rownames(x)),])

# output a data frame of observed and imputed values for
# the observations that are not in the original yai object
myImpute <- impute(malNew,vars=yvars(malNew))



# in this example, Y is not specified (not required for mahalanobis).
mal2 <- yai(x=x,method="mahalanobis")
identical(foruse(mal),foruse(mal2))

# here, method randomForest's unsupervised classification is used (no Y).
rf <- yai(x=x,method="randomForest")




# now get imputations for the targets in the iris data (those that are
# not references).
rfNew <- newtargets(rf,iris[!(rownames(iris) %in% rownames(x)),])